Overview

Brought to you by YData

Dataset statistics

Number of variables6
Number of observations64829999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 GiB
Average record size in memory96.9 B

Variable types

Categorical2
Numeric4

Alerts

id_categoria has constant value "3"Constant
liq_um is highly skewed (γ1 = 274.4975194)Skewed
liq_um has 1341925 (2.1%) zerosZeros

Reproduction

Analysis started2025-10-18 01:58:09.174494
Analysis finished2025-10-18 02:05:11.666531
Duration7 minutes and 2.49 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id_categoria
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 GiB
3
64829999 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters64829999
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
364829999
100.0%

Length

2025-10-17T23:05:11.693206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T23:05:11.890014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
364829999
100.0%

Most occurring characters

ValueCountFrequency (%)
364829999
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)64829999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
364829999
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64829999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
364829999
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64829999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
364829999
100.0%

id_cliente
Real number (ℝ)

Distinct131135
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370578.5
Minimum33
Maximum727518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.6 MiB
2025-10-17T23:05:11.928202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile57426
Q1192577
median366646
Q3550089
95-th percentile686441
Maximum727518
Range727485
Interquartile range (IQR)357512

Descriptive statistics

Standard deviation201342.97
Coefficient of variation (CV)0.54332069
Kurtosis-1.1703441
Mean370578.5
Median Absolute Deviation (MAD)180198
Skewness-0.039920542
Sum2.4024604 × 1013
Variance4.053899 × 1010
MonotonicityNot monotonic
2025-10-17T23:05:11.993840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4283034575
 
< 0.1%
3858304530
 
< 0.1%
1159244365
 
< 0.1%
4494994305
 
< 0.1%
5637424224
 
< 0.1%
996054213
 
< 0.1%
3069414194
 
< 0.1%
3826014145
 
< 0.1%
5516344120
 
< 0.1%
4522034068
 
< 0.1%
Other values (131125)64787260
99.9%
ValueCountFrequency (%)
33145
 
< 0.1%
43544
 
< 0.1%
44132
 
< 0.1%
4794
 
< 0.1%
481717
< 0.1%
51773
< 0.1%
56122
 
< 0.1%
57246
 
< 0.1%
6091
 
< 0.1%
6266
 
< 0.1%
ValueCountFrequency (%)
7275181096
< 0.1%
72751779
 
< 0.1%
727515228
 
< 0.1%
7275143
 
< 0.1%
7275121192
< 0.1%
727511441
 
< 0.1%
7275101194
< 0.1%
72750971
 
< 0.1%
727507623
< 0.1%
72750581
 
< 0.1%

id_periodo
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202392.55
Minimum202301
Maximum202508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.6 MiB
2025-10-17T23:05:12.049181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum202301
5-th percentile202302
Q1202309
median202404
Q3202412
95-th percentile202507
Maximum202508
Range207
Interquartile range (IQR)103

Descriptive statistics

Standard deviation77.05203
Coefficient of variation (CV)0.00038070586
Kurtosis-1.320406
Mean202392.55
Median Absolute Deviation (MAD)95
Skewness0.21909395
Sum1.3121109 × 1013
Variance5937.0153
MonotonicityNot monotonic
2025-10-17T23:05:12.102753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2024122359733
 
3.6%
2023032278704
 
3.5%
2023122274404
 
3.5%
2025012255210
 
3.5%
2024012251301
 
3.5%
2024032228632
 
3.4%
2024022211062
 
3.4%
2025032168995
 
3.3%
2024112144991
 
3.3%
2023012142199
 
3.3%
Other values (22)42514768
65.6%
ValueCountFrequency (%)
2023012142199
3.3%
2023022135091
3.3%
2023032278704
3.5%
2023042047442
3.2%
2023051977191
3.0%
2023061785987
2.8%
2023071794284
2.8%
2023081997513
3.1%
2023091981820
3.1%
2023102040537
3.1%
ValueCountFrequency (%)
2025081884650
2.9%
2025071839339
2.8%
2025061713365
2.6%
2025051892298
2.9%
2025041955745
3.0%
2025032168995
3.3%
2025022118941
3.3%
2025012255210
3.5%
2024122359733
3.6%
2024112144991
3.3%

tipo_mix
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 GiB
GASEOSAS
30913090 
MINERALES
13661223 
NECTAR
11319776 
FUNCIONAL
8192799 
TEA
 
743111

Length

Max length9
Median length8
Mean length7.930571
Min length3

Characters and Unicode

Total characters514138907
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNECTAR
2nd rowNECTAR
3rd rowNECTAR
4th rowFUNCIONAL
5th rowFUNCIONAL

Common Values

ValueCountFrequency (%)
GASEOSAS30913090
47.7%
MINERALES13661223
21.1%
NECTAR11319776
 
17.5%
FUNCIONAL8192799
 
12.6%
TEA743111
 
1.1%

Length

2025-10-17T23:05:12.158292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T23:05:12.199104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gaseosas30913090
47.7%
minerales13661223
21.1%
nectar11319776
 
17.5%
funcional8192799
 
12.6%
tea743111
 
1.1%

Most occurring characters

ValueCountFrequency (%)
S106400493
20.7%
A95743089
18.6%
E70298423
13.7%
N41366597
 
8.0%
O39105889
 
7.6%
G30913090
 
6.0%
R24980999
 
4.9%
I21854022
 
4.3%
L21854022
 
4.3%
C19512575
 
3.8%
Other values (4)42109708
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)514138907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S106400493
20.7%
A95743089
18.6%
E70298423
13.7%
N41366597
 
8.0%
O39105889
 
7.6%
G30913090
 
6.0%
R24980999
 
4.9%
I21854022
 
4.3%
L21854022
 
4.3%
C19512575
 
3.8%
Other values (4)42109708
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)514138907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S106400493
20.7%
A95743089
18.6%
E70298423
13.7%
N41366597
 
8.0%
O39105889
 
7.6%
G30913090
 
6.0%
R24980999
 
4.9%
I21854022
 
4.3%
L21854022
 
4.3%
C19512575
 
3.8%
Other values (4)42109708
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)514138907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S106400493
20.7%
A95743089
18.6%
E70298423
13.7%
N41366597
 
8.0%
O39105889
 
7.6%
G30913090
 
6.0%
R24980999
 
4.9%
I21854022
 
4.3%
L21854022
 
4.3%
C19512575
 
3.8%
Other values (4)42109708
 
8.2%

id_sku_venta
Real number (ℝ)

Distinct459
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443650.31
Minimum810
Maximum875220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.6 MiB
2025-10-17T23:05:12.260230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum810
5-th percentile957
Q11657
median870002
Q3870706
95-th percentile871275
Maximum875220
Range874410
Interquartile range (IQR)869049

Descriptive statistics

Standard deviation431072.97
Coefficient of variation (CV)0.97165033
Kurtosis-1.9853483
Mean443650.31
Median Absolute Deviation (MAD)1503
Skewness-0.028943162
Sum2.8761849 × 1013
Variance1.858239 × 1011
MonotonicityNot monotonic
2025-10-17T23:05:12.315548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16001377920
 
2.1%
47141232267
 
1.9%
8702031225221
 
1.9%
16031179902
 
1.8%
17561075198
 
1.7%
8706901058605
 
1.6%
16021050387
 
1.6%
1267982919
 
1.5%
1745920687
 
1.4%
4715901034
 
1.4%
Other values (449)53825859
83.0%
ValueCountFrequency (%)
81038312
 
0.1%
81645574
 
0.1%
817253331
0.4%
82639020
 
0.1%
829244568
0.4%
833453
 
< 0.1%
85644272
 
0.1%
859285778
0.4%
905223235
0.3%
909117237
0.2%
ValueCountFrequency (%)
8752205186
 
< 0.1%
87521934260
0.1%
8752184858
 
< 0.1%
8751487158
 
< 0.1%
8751477303
 
< 0.1%
8751384104
 
< 0.1%
8715693259
 
< 0.1%
8715394003
 
< 0.1%
87153726448
< 0.1%
87153413963
< 0.1%

liq_um
Real number (ℝ)

Skewed  Zeros 

Distinct10924
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28024086
Minimum-110.592
Maximum2204.928
Zeros1341925
Zeros (%)2.1%
Negative4546
Negative (%)< 0.1%
Memory size494.6 MiB
2025-10-17T23:05:12.370973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-110.592
5-th percentile0.024
Q10.072
median0.1152
Q30.216
95-th percentile0.72
Maximum2204.928
Range2315.52
Interquartile range (IQR)0.144

Descriptive statistics

Standard deviation2.5545628
Coefficient of variation (CV)9.115597
Kurtosis147340.6
Mean0.28024086
Median Absolute Deviation (MAD)0.0576
Skewness274.49752
Sum18168015
Variance6.5257909
MonotonicityNot monotonic
2025-10-17T23:05:12.429374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1446844704
 
10.6%
0.0725988861
 
9.2%
0.0965347015
 
8.2%
0.0484308539
 
6.6%
0.05764259958
 
6.6%
0.07683342929
 
5.2%
0.2883284268
 
5.1%
0.122550515
 
3.9%
0.1922241163
 
3.5%
0.0241988927
 
3.1%
Other values (10914)24673120
38.1%
ValueCountFrequency (%)
-110.5921
< 0.1%
-82.79041
< 0.1%
-58.29121
< 0.1%
-45.61921
< 0.1%
-44.9281
< 0.1%
-41.64481
< 0.1%
-31.5841
< 0.1%
-29.89441
< 0.1%
-28.81
< 0.1%
-27.6481
< 0.1%
ValueCountFrequency (%)
2204.9281
< 0.1%
2128.8961
< 0.1%
2068.07041
< 0.1%
2054.5921
< 0.1%
2021.762
< 0.1%
2005.6321
< 0.1%
1868.14081
< 0.1%
1850.3521
< 0.1%
1825.3442
< 0.1%
1824.7681
< 0.1%

Interactions

2025-10-17T23:04:08.752279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:02:48.971266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:17.396025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:43.200640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:04:15.149678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:02:56.121339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:23.845460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:49.631101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:04:21.966192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:02.830677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:30.176640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:55.797477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:04:28.386204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:10.145355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:03:36.818957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:04:02.331700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-17T23:05:12.470315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
id_clienteid_periodoid_sku_ventaliq_umtipo_mix
id_cliente1.0000.026-0.001-0.0090.011
id_periodo0.0261.0000.086-0.0160.020
id_sku_venta-0.0010.0861.000-0.1850.426
liq_um-0.009-0.016-0.1851.0000.003
tipo_mix0.0110.0200.4260.0031.000

Missing values

2025-10-17T23:04:30.149088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-17T23:04:40.771061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_um
03570220202410NECTAR8703350.1680
13570220202410NECTAR8703360.1680
23570220202410NECTAR8710970.0840
33570220202411FUNCIONAL8702020.2400
43570220202411FUNCIONAL8702030.3360
53570220202411FUNCIONAL8702050.2520
63570220202411FUNCIONAL8702060.2160
73570220202411MINERALES8712400.2304
83570220202411MINERALES8712420.2304
93570220202411NECTAR8700021.0944
id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_um
648299893570220202410FUNCIONAL8702030.0960
648299903570220202410FUNCIONAL8702050.1800
648299913570220202410FUNCIONAL8702060.2520
648299923570220202410MINERALES8712400.2880
648299933570220202410MINERALES8712420.4032
648299943570220202410NECTAR8700021.1520
648299953570220202410NECTAR8700030.4032
648299963570220202410NECTAR8700050.4032
648299973570220202410NECTAR8700080.6912
648299983570220202410NECTAR8703340.1260